Text segmentation aims to divide text into contiguous, semantically coherent
segments, while segment labeling deals with producing labels for each segment.
Past work has shown success in tackling segmentation and labeling for documents
and conversations. This has been possible with a combination of task-specific
pipelines, supervised and unsupervised learning objectives. In this work, we
propose a single encoder-decoder neural network that can handle long documents
and conversations, trained simultaneously for both segmentation and segment
labeling using only standard supervision. We successfully show a way to solve
the combined task as a pure generation task, which we refer to as structured
summarization. We apply the same technique to both document and conversational
data, and we show state of the art performance across datasets for both
segmentation and labeling, under both high- and low-resource settings. Our
results establish a strong case for considering text segmentation and segment
labeling as a whole, and moving towards general-purpose techniques that don't
depend on domain expertise or task-specific components.

提出一种单一的编码器 - 解码器神经网络方法，通过结构化总结的形式同时训练文档和对话数据的文本切片和标签任务，并成功展示了该方法在所属领域的顶尖性能以及其重要性。

结构化摘要：统一的文本分段和段落标注作为生成任务

Structured Summarization: Unified Text Segmentation and Segment Labeling as a Generation Task

In this paper, we introduce and tackle the Outline Generation (OG) task,
which aims to unveil the inherent content structure of a multi-paragraph
document by identifying its potential sections and generating the corresponding
section headings. Without loss of generality, the OG task can be viewed as a
novel structured summarization task. To generate a sound outline, an ideal OG
model should be able to capture three levels of coherence, namely the coherence
between context paragraphs, that between a section and its heading, and that
between context headings. The first one is the foundation for section
identification, while the latter two are critical for consistent heading
generation. In this work, we formulate the OG task as a hierarchical structured
prediction problem, i.e., to first predict a sequence of section boundaries and
then a sequence of section headings accordingly. We propose a novel
hierarchical structured neural generation model, named HiStGen, for the task.
Our model attempts to capture the three-level coherence via the following ways.
First, we introduce a Markov paragraph dependency mechanism between context
paragraphs for section identification. Second, we employ a section-aware
attention mechanism to ensure the semantic coherence between a section and its
heading. Finally, we leverage a Markov heading dependency mechanism and a
review mechanism between context headings to improve the consistency and
eliminate duplication between section headings. Besides, we build a novel
WIKIOG dataset, a public collection which consists of over 1.75 million
document-outline pairs for research on the OG task. Experimental results on our
benchmark dataset demonstrate that our model can significantly outperform
several state-of-the-art sequential generation models for the OG task.

本研究提出并解决了概述生成 (OG) 任务，该任务旨在通过识别多段文档的潜在部分并生成相应的部分标题来揭示其内在内容结构。我们将 OG 任务作为一种新颖的结构化摘要任务来处理，在模型中通过引入马尔科夫段落依赖机制、区域感知注视机制、马尔科夫标题依赖机制以及评论机制等方法来捕捉三个层次的一致性，即上下文段落之间的一致性、一个部分和它的标题之间的一致性以及上下文标题之间的一致性。实验结果表明，我们的模型可以显著优于 OG 任务的几种最先进的顺序生成模型。